Update app.py

app.py

@@ -15,28 +15,25 @@ ZEPHYR_API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zeph
 SD_API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
 
 def query_zephyr(linkedin_text):
-    # Constructing a single string prompt that combines the instruction with the LinkedIn post text
-    
+    # Headers for the HTTP request
     headers = {
         "Authorization": f"Bearer {ZEPHYR_API_TOKEN}",
         "Content-Type": "application/json",
     }
 
-   prompt = "Prepare a prompt for Stable Diffusion for the following LinkedIn post:"
-conversation = [
-    {"role": "system", "content": prompt},
-    {"role": "user", "content": linkedin_text}
-]
-
-payload = {
-    "inputs": {
-        "past_user_inputs": [],
-        "generated_responses": [],
-        "text": linkedin_text,  # This might need adjustment based on the model's requirements.
-        "conversation": conversation,
+    # Constructing the conversation
+    prompt = "Prepare a prompt for Stable Diffusion for the following LinkedIn post:"
+    conversation = [
+        {"role": "system", "content": prompt},
+        {"role": "user", "content": linkedin_text}
+    ]
+    
+    # Adjusting the payload structure based on the expected format
+    payload = {
+        "inputs": conversation,
     }
-}
 
+    # Sending the HTTP POST request
     response = requests.post(ZEPHYR_API_URL, headers=headers, json=payload)
     if response.status_code == 200:
         return response.json()